{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conv1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('tcs_stock_2018-05-26.csv')\n",
    "df.head()\n",
    "\n",
    "\n",
    "# In[2]:\n",
    "\n",
    "\n",
    "# 将date 字段设置为索引\n",
    "df = df.set_index('Date')\n",
    "df.head()\n",
    "\n",
    "\n",
    "# In[3]:\n",
    "\n",
    "\n",
    "# 弃用一些字段\n",
    "drop_columns = ['Last','Total Trade Quantity','Turnover (Lacs)']\n",
    "df = df.drop(drop_columns,axis=1)\n",
    "df.head()\n",
    "\n",
    "\n",
    "# In[4]:\n",
    "\n",
    "\n",
    "df['High'] = df['High'] / 10000\n",
    "df['Open'] = df['Open'] / 10000\n",
    "df['Low'] = df['Low'] / 10000\n",
    "df['Close'] = df['Close'] / 10000\n",
    "print(df.head())\n",
    "\n",
    "\n",
    "# In[5]:\n",
    "\n",
    "\n",
    "# 将dataframe 转化为 array\n",
    "data = df.as_matrix()\n",
    "\n",
    "\n",
    "# In[6]:\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import datetime\n",
    "import math\n",
    "import itertools\n",
    "from sklearn import preprocessing\n",
    "import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "# 1 : 数据切分\n",
    "result = []\n",
    "time_steps = 6\n",
    "\n",
    "\n",
    "for i in range(len(data)-time_steps):\n",
    "    result.append(data[i:i+time_steps])\n",
    "\n",
    "result = np.array(result)\n",
    "\n",
    "\n",
    "# 训练集和测试集的数据量划分\n",
    "train_size = int(0.8*len(result))\n",
    "\n",
    "# 训练集切分\n",
    "train = result[:train_size,:]\n",
    "\n",
    "x_train = train[:, :-1]\n",
    "y_train = train[:, -1][:,-1]\n",
    "   \n",
    "x_test = result[train_size:,:-1]\n",
    "y_test = result[train_size:,-1][:,-1]\n",
    "\n",
    "feature_nums = len(df.columns)\n",
    "\n",
    "# 数据重塑\n",
    "x_train = x_train.reshape(x_train.shape[0],x_train.shape[1],x_train.shape[2])\n",
    "x_test = x_test.reshape(x_test.shape[0],x_test.shape[1],x_test.shape[2])\n",
    "\n",
    "print(\"X_train\", x_train.shape)\n",
    "print(\"y_train\", y_train.shape)\n",
    "print(\"X_test\", x_test.shape)\n",
    "print(\"y_test\", y_test.shape)\n",
    "\n",
    " # 举例：用前5行数据，预测第6行的最后一个数据\n",
    "# train\n",
    " #[[[0.126695 0.12679  0.126    0.126415]\n",
    "#   [0.1267   0.12724  0.125555 0.12633 ]\n",
    "#   [0.1265   0.1284   0.125995 0.12806 ]\n",
    "#   [0.1285   0.1301   0.12809  0.12992 ]\n",
    "#   [0.13     0.1304   0.129025 0.129485]\n",
    "#   [0.1295   0.13043  0.12943  0.130025]]\n",
    "\n",
    "# x_train\n",
    "# [[[0.126695 0.12679  0.126    0.126415]\n",
    "#   [0.1267   0.12724  0.125555 0.12633 ]\n",
    "#   [0.1265   0.1284   0.125995 0.12806 ]\n",
    "#   [0.1285   0.1301   0.12809  0.12992 ]\n",
    "#   [0.13     0.1304   0.129025 0.129485]]\n",
    "\n",
    "# y_train\n",
    "#[0.130025]\n",
    "\n",
    "\n",
    "# In[7]:\n",
    "\n",
    "\n",
    "from __future__ import print_function\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Activation,Dropout,Flatten,Conv1D,MaxPooling1D\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras import losses\n",
    "from keras import optimizers\n",
    "\n",
    "def build_model(input):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128,input_shape=(input[0],input[1])))\n",
    "    model.add(Conv1D(filters=112,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "    model.add(Conv1D(filters=64,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(MaxPooling1D(pool_size=1,padding='valid'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(100,activation='relu',kernel_initializer='uniform'))\n",
    "    model.add(Dense(1,activation='relu',kernel_initializer='uniform'))\n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    return model \n",
    "\n",
    "model = build_model([5,4,1])\n",
    "\n",
    "# Summary of the Model\n",
    "print(model.summary())    \n",
    "\n",
    "\n",
    "# In[8]:\n",
    "\n",
    "\n",
    "# 训练数据\n",
    "from timeit import default_timer as timer\n",
    "start = timer()\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
    "                    batch_size=128,\n",
    "                    epochs=35,\n",
    "                    validation_split=0.2,\n",
    "                    verbose=2)\n",
    "end = timer()\n",
    "print(end - start)\n",
    "\n",
    "\n",
    "# In[9]:\n",
    "\n",
    "\n",
    "# 返回history\n",
    "history_dict = history.history\n",
    "history_dict.keys()\n",
    "\n",
    "\n",
    "# In[10]:\n",
    "\n",
    "\n",
    "# 画出训练集和验证集的损失曲线\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "loss_values50 = loss_values[0:150]\n",
    "val_loss_values50 = val_loss_values[0:150]\n",
    "epochs = range(1, len(loss_values50) + 1)\n",
    "plt.plot(epochs, loss_values50, 'b',color = 'blue', label='Training loss')\n",
    "plt.plot(epochs, val_loss_values50, 'b',color='red', label='Validation loss')\n",
    "plt.rc('font', size = 18)\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.xticks(epochs)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,7)\n",
    "#fig.savefig('img/tcstest&validationlosscnn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128,input_shape=(input_shape[0],input_shape[1])))\n",
    "    model.add(Conv1D(filters=80,kernel_size=1,padding='same',activation='relu',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "    model.add(Conv1D(filters=48,kernel_size=1,padding='same',activation='relu',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.add(MaxPooling1D(pool_size=2, padding='valid'))\n",
    "    model.add(LSTM(32,return_sequences=True))\n",
    "    model.add(LSTM(16,return_sequences=False))\n",
    "    model.add(Dense(32, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "    model.add(Dense(1, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    #模型结构\n",
    "#     model = Sequential()\n",
    "#     model.add(Dense(128,input_shape=(input_shape[0],input_shape[1])))\n",
    "#     model.add(Conv1D(filters=112,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "#     model.add(Conv1D(filters=64,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(MaxPooling1D(pool_size=1,padding='valid'))\n",
    "#     model.add(Dropout(0.2))\n",
    "#     model.add(Flatten())\n",
    "#     model.add(Dense(100,activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(Dense(1,activation='relu',kernel_initializer='uniform'))\n",
    "#     model.compile(loss='mse',optimizer='adam',metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 6, 128)            2048      \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 6, 80)             10320     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 3, 80)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 3, 48)             3888      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 1, 48)             0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 1, 32)             10368     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 16)                3136      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 30,337\n",
      "Trainable params: 30,337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = build_model([6,15])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\anaconda\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(249, 7, 18) (249,)\n",
      "WARNING:tensorflow:From C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Train on 199 samples, validate on 50 samples\n",
      "WARNING:tensorflow:From C:\\anaconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Epoch 1/2000\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "2 root error(s) found.\n  (0) Internal: Blas GEMM launch failed : a.shape=(112, 18), b.shape=(18, 128), m=112, n=128, k=18\n\t [[{{node dense/Tensordot/MatMul}}]]\n\t [[loss_1/mul/_161]]\n  (1) Internal: Blas GEMM launch failed : a.shape=(112, 18), b.shape=(18, 128), m=112, n=128, k=18\n\t [[{{node dense/Tensordot/MatMul}}]]\n0 successful operations.\n0 derived errors ignored.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-4ea57ec9a260>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    166\u001b[0m     \u001b[0mtrain_val_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0monehot_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscale_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mall_data_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[1;31m#训练模型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_val_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m     \u001b[1;31m#预测结果\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m     \u001b[1;31m#res = inverse_scale(scale_data[0],res[0].T)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-4ea57ec9a260>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(train_val_data)\u001b[0m\n\u001b[0;32m    137\u001b[0m                         \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m                         \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mearly_stopping\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m                         shuffle=False)\n\u001b[0m\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m     \u001b[0my_val_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    778\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m           \u001b[0mvalidation_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m           steps_name='steps_per_epoch')\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m         \u001b[1;31m# Get outputs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3291\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[1;32m-> 3292\u001b[1;33m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[0;32m   3293\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3294\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[0;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1458\u001b[1;33m                                                run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1459\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1460\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: 2 root error(s) found.\n  (0) Internal: Blas GEMM launch failed : a.shape=(112, 18), b.shape=(18, 128), m=112, n=128, k=18\n\t [[{{node dense/Tensordot/MatMul}}]]\n\t [[loss_1/mul/_161]]\n  (1) Internal: Blas GEMM launch failed : a.shape=(112, 18), b.shape=(18, 128), m=112, n=128, k=18\n\t [[{{node dense/Tensordot/MatMul}}]]\n0 successful operations.\n0 derived errors ignored."
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from tensorflow.keras.models import Sequential,load_model\n",
    "from tensorflow.keras.layers import Dense,LSTM,Dropout\n",
    "from tensorflow.keras.layers import Dense,Activation,Dropout,Flatten,Conv1D,MaxPooling1D\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import optimizers,metrics\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "def read_data():\n",
    "    file_dir = './data/train_data.csv'\n",
    "    all_data_frame = pd.read_csv(file_dir)\n",
    "    all_data_frame = all_data_frame[all_data_frame['ycsb']=='202106150038GF']\n",
    "    return all_data_frame\n",
    "\n",
    "def one_hot(data):\n",
    "    result = pd.get_dummies(data['weather'], sparse=True)\n",
    "    return result\n",
    "\n",
    "def scale(all_data_df):\n",
    "    selected_features = ['hour', 'gfrl','cur_a','cur_b','cur_c','vol_a','vol_b','vol_c','p','szgl','low_tp', 'high_tp', 'avg_tp', 'wind']\n",
    "    scaled_data = all_data_df[selected_features]\n",
    "    scaler = MinMaxScaler()\n",
    "    scaled_data = pd.DataFrame(scaler.fit_transform(scaled_data),columns=scaled_data.keys())\n",
    "    return scaled_data\n",
    "\n",
    "def inverse_scale(scaler,res):\n",
    "    pre_res  =scaler.inverse_transform(res)\n",
    "    return pre_res\n",
    "\n",
    "def slice_window(x_train,y_train,step):\n",
    "    X,y=[],[] \n",
    "    for i in range(0,len(x_train)-step,1): \n",
    "        end = i + step\n",
    "        oneX = x_train[i:end,:]\n",
    "        oney = y_train[end]\n",
    "        X.append(oneX)\n",
    "        y.append(oney)\n",
    "    return np.array(X),np.array(y)\n",
    "\n",
    "def data_transform(onehot_data,scale_data,all_data_df):\n",
    "    #充值索引，合并数据\n",
    "    onehot_data = onehot_data.reset_index()\n",
    "    scale_data = scale_data.reset_index()\n",
    "    merge_data = pd.concat([onehot_data,scale_data], axis=1)\n",
    "    X_train = merge_data.drop('index',axis=1)\n",
    "    y = all_data_df['fdgl']\n",
    "    \n",
    "    #slice_window\n",
    "    step = 7\n",
    "    x_y = slice_window(np.array(X_train),np.array(y),step)\n",
    "    print(x_y[0].shape,x_y[1].shape)\n",
    "    X_train, y = x_y[0], x_y[1]\n",
    "    \n",
    "    #reshape参数\n",
    "#     sample = int(X_train.shape[0])//1\n",
    "#     time_step = 1\n",
    "#     feature_num = X_train.shape[1]\n",
    "#     #reshape\n",
    "#     X_train=X_train.values.reshape(sample,time_step,feature_num)\n",
    "#     y = y.values.reshape(sample,time_step)\n",
    "#     print('X:',X_train.shape)\n",
    "#     print('Y:',y.shape)\n",
    "    \n",
    "    #分割验证集\n",
    "    X_train,X_val,y_train,y_val=train_test_split(X_train,y,test_size=0.2, random_state=333)\n",
    "    \n",
    "    return [X_train,X_val,y_train,y_val]\n",
    "\n",
    "def build_model(input_shape):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(128,input_shape=(input_shape[0],input_shape[1])))\n",
    "    model.add(Conv1D(filters=80,kernel_size=1,padding='same',activation='relu',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "    model.add(Conv1D(filters=48,kernel_size=1,padding='same',activation='relu',kernel_initializer=\"glorot_uniform\"))\n",
    "    model.add(MaxPooling1D(pool_size=2, padding='valid'))\n",
    "    model.add(LSTM(32,return_sequences=True))\n",
    "    model.add(LSTM(16,return_sequences=False))\n",
    "    model.add(Dense(32, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "    model.add(Dense(1, activation=\"relu\", kernel_initializer=\"uniform\"))\n",
    "    model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    #模型结构\n",
    "#     model = Sequential()\n",
    "#     model.add(Dense(128,input_shape=(input_shape[0],input_shape[1])))\n",
    "#     model.add(Conv1D(filters=112,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(MaxPooling1D(pool_size=2,padding='valid'))\n",
    "#     model.add(Conv1D(filters=64,kernel_size=1,padding='valid',activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(MaxPooling1D(pool_size=1,padding='valid'))\n",
    "#     model.add(Dropout(0.2))\n",
    "#     model.add(Flatten())\n",
    "#     model.add(Dense(100,activation='relu',kernel_initializer='uniform'))\n",
    "#     model.add(Dense(1,activation='relu',kernel_initializer='uniform'))\n",
    "#     model.compile(loss='mse',optimizer='adam',metrics=['mae'])\n",
    "    \n",
    "    return model \n",
    "\n",
    "def train_model(train_val_data):\n",
    "    #获取数据\n",
    "    X_train,X_val,y_train,y_val = train_val_data[0],train_val_data[1],train_val_data[2],train_val_data[3]\n",
    "    \n",
    "    #超参数\n",
    "    time_step = X_train.shape[1]\n",
    "    feature_nums = X_train.shape[2]\n",
    "    epochs = 2000\n",
    "    batch_size = 16\n",
    "    patience = 50\n",
    "    \n",
    "    #获取模型\n",
    "    model = build_model([time_step,feature_nums])\n",
    "\n",
    "    #优化器\n",
    "    adam = optimizers.Adam(learning_rate = 0.0001)\n",
    "    #adam = optimizers.SGD(learning_rate = 0.001,momentum=0.2)\n",
    "\n",
    "    #损失函数\n",
    "    model.compile(loss='mse', optimizer=adam)\n",
    "\n",
    "    #early_stop\n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=patience, verbose=1)\n",
    "    \n",
    "    # 可视化\n",
    "#     tensorboard_callback = keras.callbacks.TensorBoard('./log', histogram_freq=1)\n",
    "#     %load_ext tensorboard\n",
    "#     %tensorboard --logdir logs\n",
    "\n",
    "    #超参数\n",
    "    history = model.fit(X_train, y_train, \n",
    "                        epochs=epochs,\n",
    "                        batch_size=batch_size,\n",
    "                        validation_data=(X_val, y_val),\n",
    "                        verbose=1,\n",
    "                        callbacks=[early_stopping], \n",
    "                        shuffle=False)\n",
    "    \n",
    "    y_val_pred = model.predict(X_val)\n",
    "    rmse = mean_squared_error(y_val, y_val_pred) ** 0.5\n",
    "    score=1.0/(1.0+rmse)\n",
    "    print('score:',score)\n",
    "    print('rmse:',rmse)\n",
    "    model.save('./model/lstm_two')\n",
    "    draw(history)\n",
    "    print(model.summary())\n",
    "    return [y_val,y_val_pred]\n",
    "\n",
    "def draw(history):\n",
    "    from matplotlib import pyplot\n",
    "    pyplot.plot(history.history['loss'], label='train')\n",
    "    pyplot.plot(history.history['val_loss'], label='test')\n",
    "    pyplot.legend()\n",
    "    pyplot.show()\n",
    "\n",
    "if __name__=='__main__':\n",
    "    #读取原数据\n",
    "    all_data_df = read_data()\n",
    "    #非数值数据onehot处理\n",
    "    onehot_data = one_hot(all_data_df)\n",
    "    #数值数据归一化处理\n",
    "    scale_data = scale(all_data_df)\n",
    "    #拼接，shape转换，分割\n",
    "    train_val_data = data_transform(onehot_data,scale_data,all_data_df)\n",
    "    #训练模型\n",
    "    res = train_model(train_val_data)\n",
    "    #预测结果\n",
    "    #res = inverse_scale(scale_data[0],res[0].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测结果和真实值\n",
    "from matplotlib import pyplot\n",
    "pyplot.figure(figsize=(50,10))\n",
    "pyplot.plot(res[0], label='u1_real',linestyle='--')\n",
    "pyplot.plot(res[1], label='u1_pre',linestyle='-')\n",
    "\n",
    "pyplot.rcParams.update({'font.size': 20})\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conv2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n",
      "(60000,)\n",
      "(10000,)\n",
      "[[[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]\n",
      "\n",
      " [[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]\n",
      "\n",
      " [[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]\n",
      "\n",
      " [[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]\n",
      "\n",
      " [[0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  ...\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]\n",
      "  [0 0 0 ... 0 0 0]]]\n",
      "(60000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n",
      "(60000,)\n",
      "(10000,)\n"
     ]
    },
    {
     "data": {
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       "         [0.]]]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow.keras\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation,Dense,Flatten,Conv2D,MaxPooling2D\n",
    "\n",
    "(train_x, train_y),(test_x, test_y) = mnist.load_data()\n",
    "print(train_x.shape) #(60000, 28, 28)\n",
    "print(test_x.shape) #(10000, 28, 28)\n",
    "print(train_y.shape) #(60000,)\n",
    "print(test_y.shape) # (10000,)\n",
    "print(train_x)\n",
    "\n",
    "train_x = train_x.reshape(60000, 28, 28, 1) \n",
    "test_x = test_x.reshape(10000, 28, 28, 1)\n",
    "train_x = train_x.astype('float32')\n",
    "test_x = test_x.astype('float32')\n",
    "train_x /= 255 #把数值变成0-1之间的浮点数。\n",
    "test_x /=255\n",
    "print(train_x.shape) #(60000, 28, 28)\n",
    "print(test_x.shape) #(10000, 28, 28)\n",
    "print(train_y.shape) #(60000,)\n",
    "print(test_y.shape) # (10000,)\n",
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = tensorflow.keras.utils.to_categorical(train_y, num_classes=10)\n",
    "test_y = tensorflow.keras.utils.to_categorical(test_y, num_classes=10)\n",
    "\n",
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n",
      "(60000,)\n",
      "(10000,)\n",
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
      "  175  26 166 255 247 127   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
      "  225 172 253 242 195  64   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
      "   93  82  82  56  39   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
      "   25   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
      "  150  27   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
      "  253 187   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
      "  253 249  64   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      "  253 207   2   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
      "  250 182   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
      "   78   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               1605888   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 1,608,778\n",
      "Trainable params: 1,608,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "ename": "UnknownError",
     "evalue": "2 root error(s) found.\n  (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[{{node conv2d_1/Conv2D}}]]\n  (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[{{node conv2d_1/Conv2D}}]]\n\t [[loss_1/mul/_145]]\n0 successful operations.\n0 derived errors ignored.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-8acfc8e5cf1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'categorical_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adam'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 40\u001b[1;33m \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_epochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     41\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    778\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m           \u001b[0mvalidation_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m           steps_name='steps_per_epoch')\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m         \u001b[1;31m# Get outputs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3291\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[1;32m-> 3292\u001b[1;33m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[0;32m   3293\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3294\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[0;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1458\u001b[1;33m                                                run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1459\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1460\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnknownError\u001b[0m: 2 root error(s) found.\n  (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[{{node conv2d_1/Conv2D}}]]\n  (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.\n\t [[{{node conv2d_1/Conv2D}}]]\n\t [[loss_1/mul/_145]]\n0 successful operations.\n0 derived errors ignored."
     ]
    }
   ],
   "source": [
    "import tensorflow.keras\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation,Dense,Flatten,Conv2D,MaxPooling2D\n",
    "\n",
    "(train_x, train_y),(test_x, test_y) = mnist.load_data()\n",
    "print(train_x.shape) #(60000, 28, 28)\n",
    "print(test_x.shape) #(10000, 28, 28)\n",
    "print(train_y.shape) #(60000,)\n",
    "print(test_y.shape) # (10000,)\n",
    "print(train_x[0])\n",
    "\n",
    "train_y\n",
    "\n",
    "train_x = train_x.reshape(60000, 28, 28, 1) \n",
    "test_x = test_x.reshape(10000, 28, 28, 1)\n",
    "train_x = train_x.astype('float32')\n",
    "test_x = test_x.astype('float32')\n",
    "train_x /= 255 #把数值变成0-1之间的浮点数。\n",
    "test_x /=255\n",
    "\n",
    "train_y = tensorflow.keras.utils.to_categorical(train_y, num_classes=10)\n",
    "test_y = tensorflow.keras.utils.to_categorical(test_y, num_classes=10)\n",
    "\n",
    "train_y\n",
    "\n",
    "n_hidden_1 = 256 #设定隐藏层\n",
    "n_classes = 10 #设定最后的输出层\n",
    "training_epochs = 15 #设定整体训练数据共训练多少次\n",
    "batch_size = 100 \n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=(28,28,1), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(n_hidden_1, activation='relu'))\n",
    "model.add(Dense(n_classes, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n",
    "history = model.fit(train_x, train_y, batch_size=batch_size, epochs=training_epochs, validation_data=(test_x, test_y))\n",
    "history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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